TLS fingerprinting for process identification

ABSTRACT

In one embodiment, a device obtains telemetry data regarding an encrypted traffic session in a network. The telemetry data includes Transport Layer Security (TLS) features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session. The device retrieves, using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes. The device uses a probabilistic model to assign probabilities to each of the plurality of candidate processes. The device identifies one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to using Transport Layer Security (TLS) fingerprinting for process identification.

BACKGROUND

Network traffic is becoming increasingly encrypted. Indeed, some studies estimate that more than 70% of all network traffic is now encrypted, with this figure expected to continue to grow.

Identifying the process associated with an encrypted traffic session can be quite useful. In the security domain, for example, process identification can be used to detect malware or vulnerable executables on an endpoint. Likewise, in the networking domain, routers and switches can use this information to prioritize traffic or aid in diagnostics, such as analyzing how processes communicate across different network segments.

In some protocols, such as Secure Socket Layer (SSL) and the HyperText Transfer Protocol (HTTP), a clear-text/unencrypted description of the process is sent with the traffic, affording at least some degree of ground truth as to the sending process. However, the Transport Layer Security (TLS) protocol lacks this clear-text description of the source process. TLS fingerprinting offers one potential way to make inferences from the cryptographic parameters offered in the TLS ClientHello message. Unfortunately, though, experimentation has shown that a diverse set of executables/processes typically map to a single TLS fingerprint. For example, automatic software updates can often lead to multiple versions of a software process sharing the same TLS fingerprint, which can make it very difficult to perform tasks like identifying endpoints that are still executing processes that are vulnerable to security exploits.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example of a device capturing traffic information;

FIG. 4 illustrates an example encrypted session;

FIG. 5 illustrates an example architecture for identifying the process that initiated an encrypted traffic session in a network; and

FIG. 6 illustrates an example simplified procedure for identifying a process that initiated an encrypted traffic session.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a device obtains telemetry data regarding an encrypted traffic session in a network. The telemetry data includes Transport Layer Security (TLS) features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session. The device retrieves, using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes. The device uses a probabilistic model to assign probabilities to each of the plurality of candidate processes. The device identifies one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability.

DESCRIPTION

A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may further be interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100 illustratively comprising nodes/devices, such as a plurality of routers/devices interconnected by links or networks, as shown. For example, customer edge (CE) routers 110 may be interconnected with provider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order to communicate across a core network, such as an illustrative network backbone 130. For example, routers 110, 120 may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like. Data packets 140 (e.g., traffic/messages) may be exchanged among the nodes/devices of the computer network 100 over links using predefined network communication protocols such as the Transmission Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relay protocol, or any other suitable protocol. Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN, thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:

1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/5G/LTE backup connection). For example, a particular CE router 110 shown in network 100 may support a given customer site, potentially also with a backup link, such as a wireless connection.

2.) Site Type B: a site connected to the network using a CE router with two MPLS VPN links (e.g., from different service providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). A site of type B may itself be of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different service providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). For example, a particular customer site may be connected to network 100 via PE-3 and via a separate Internet connection, potentially also with a wireless backup link.

2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service level agreement, whereas Internet links may either have no service level agreement at all or a loose service level agreement (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/LTE backup link). For example, a particular customer site may include a first CE router 110 connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail, according to various embodiments. As shown, network backbone 130 may provide connectivity between devices located in different geographical areas and/or different types of local networks. For example, network 100 may comprise local networks 160, 162 that include devices/nodes 10-16 and devices/nodes 18-20, respectively, as well as a data center/cloud environment 150 that includes servers 152-154. Notably, local networks 160-162 and data center/cloud environment 150 may be located in different geographic locations.

Servers 152-154 may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network 100 may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc.

The techniques herein may also be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc. Further, in various embodiments, network 100 may include one or more mesh networks, such as an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” refers to uniquely identifiable objects/things and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, etc., are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained. In particular, LLN routers typically operate with highly constrained resources, e.g., processing power, memory, and/or energy (battery), and their interconnections are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (e.g., between devices inside the LLN), point-to-multipoint traffic (e.g., from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (e.g., from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network 160 may be an LLN in which CE-2 operates as a root node for nodes/devices 10-16 in the local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example node/device 200 that may be used with one or more embodiments described herein, e.g., as any of the computing devices shown in FIGS. 1A-1B, particularly the PE routers 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g., a network controller located in a data center, etc.), any other computing device that supports the operations of network 100 (e.g., switches, etc.), or any of the other devices referenced below. The device 200 may also be any other suitable type of device depending upon the type of network architecture in place, such as IoT nodes, etc. Device 200 comprises one or more network interfaces 210, one or more processors 220, and a memory 240 interconnected by a system bus 250, and is powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network 100. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface 210 may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242 (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory 240 and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a process identifier 248.

It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.

In general, process identifier 248 may execute one or more machine learning-based classifiers to identify the source process associated with encrypted traffic in the network for any number of purposes. In one embodiment, process identifier 248 may assess captured telemetry data regarding one or more traffic flows, to determine whether a given traffic flow or set of flows are caused by malware in the network, such as a particular family of malware applications. Example forms of traffic that can be caused by malware may include, but are not limited to, traffic flows reporting exfiltrated data to a remote entity, spyware or ransomware-related flows, command and control (C2) traffic that oversees the operation of the deployed malware, traffic that is part of a network attack, such as a zero day attack or denial of service (DoS) attack, combinations thereof, or the like. In further embodiments, process identifier 248 may identify the process associated with the encrypted traffic for purposes of determining whether the process represents a security vulnerability in the network and should be updated or removed from the endpoint. For example, use of an outdated version of a process could leave the client vulnerable to exploits and identification of the process enables updating of the version automatically or manually.

Process identifier 248 may employ any number of machine learning techniques, to identify the process associated with encrypted traffic based on the observed telemetry data. In general, machine learning is concerned with the design and the development of techniques that receive empirical data as input (e.g., telemetry data regarding traffic in the network) and recognize complex patterns in the input data. For example, some machine learning techniques use an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function is a function of the number of misclassified points. The learning process then operates by adjusting the parameters a,b,c such that the number of misclassified points is minimal. After this optimization/learning phase, process identifier 248 can use the model M to classify new data points, such as information regarding new traffic flows and/or processes in the network. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data.

In various embodiments, process identifier 248 may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data. For example, the training data may include sample telemetry data that has been labeled with an associated process, if known. On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes in the behavior of the network traffic. Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data.

Example machine learning techniques that process identifier 248 can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) ANNs (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like.

In some cases, process identifier 248 may assess the captured telemetry data on a per-flow basis. In other embodiments, process identifier 248 may assess telemetry data for a plurality of traffic flows based on any number of different conditions. For example, traffic flows may be grouped based on their sources, destinations, temporal characteristics (e.g., flows that occur around the same time, etc.), combinations thereof, or based on any other set of flow characteristics.

As shown in FIG. 3, various mechanisms can be leveraged to capture information about traffic in a network, such as telemetry data regarding an encrypted traffic flow. For example, consider the case in which client node 10 initiates a traffic flow with remote server 154 that includes any number of packets 302. Any number of networking devices along the path of the flow may analyze and assess packet 302, to capture telemetry data regarding the traffic flow. For example, as shown, router CE-2 through which the traffic between node 10 and server 154 flows may capture telemetry data regarding the traffic flow.

In some embodiments, a networking device may analyze packet headers, to capture feature information about the traffic flow. For example, router CE-2 may capture the source address and/or port of host node 10, the destination address and/or port of server 154, the protocol(s) used by packet 302, or other header information by analyzing the header of a packet 302. Example captured features may include, but are not limited to, Transport Layer Security (TLS) information (e.g., from a TLS handshake), such as the ciphersuite offered, user agent, TLS extensions (e.g., type of encryption used, the encryption key exchange mechanism, the encryption authentication type, etc.), HTTP information (e.g., URI, etc.), Domain Name System (DNS) information, or any other data features that can be extracted from the observed traffic flow(s).

In further embodiments, the device may also assess the payload of the packet to capture information about the traffic flow. For example, router CE-2 or another device may perform deep packet inspection (DPI) on one or more of packets 302, to assess the contents of the packet. Doing so may, for example, yield additional information that can be used to determine the application associated with the traffic flow (e.g., packets 302 were sent by a web browser of node 10, packets 302 were sent by a videoconferencing application, etc.). However, as would be appreciated, a traffic flow may also be encrypted, thus preventing the device from assessing the actual payload of the packet. In such cases, the characteristics of the application can instead be inferred from the captured header information.

The networking device that captures the flow telemetry data may also compute any number of statistics or metrics regarding the traffic flow. For example, CE-2 may determine the start time, end time, duration, packet size(s), the distribution of bytes within a flow, etc., associated with the traffic flow by observing packets 302. In further examples, the capturing device may capture sequence of packet lengths and time (SPLT) data regarding the traffic flow, sequence of application lengths and time (SALT) data regarding the traffic flow, or byte distribution (BD) data regarding the traffic flow.

FIG. 4 illustrates an example encrypted session 400 between a client 402 and a server 404. As shown, client 402 may begin by initiating a handshake with server 404 in which cryptographic information is first exchanged. This cryptographic information can then be used by client 402 and server 404, to encrypt subsequent messages between the two. In particular, client 402 may send a ClientHello message 406 to server 404, thereby signifying that client 402 wishes to establish an encrypted session with server 404. Note that, in some cases, client 402 and server 404 may first perform a SYN-ACK, to establish the TCP/IP connection via which ClientHello message 406 may be sent.

In various cases, ClientHello message 406 may include cryptographic keys for client 402 that can be used by server 404 to immediately begin encrypting the messages sent by server 404 back to client 402. This is the approach taken by some encryption mechanisms, such as TLS version 1.3. In prior versions of TLS, and in other schemes, the key exchange is performed only after an exchange of Hello messages.

In response to receiving ClientHello message 406, server 404 may generate and send a ServerHello message 408 back to client 402. Such a ServerHello message 408 may include the server key information for server 404, extensions, and the server certificate of server 404, which may be encrypted using the client keys sent by client 402 as part of ClientHello message 406. Client 402 can then use its own keys to decrypt ClientHello message 406 and begin encrypting its subsequent messages based on the information included in ServerHello message 408. For example, client 402 may use the server certificate included in ServerHello message 408 to authenticate server 404 and the server keys and extensions included in ServerHello message 408 to control the encryption of a GET HTTP message 410 sent by client 402 to server 404. In turn, server 404 can use the information that it obtained from the handshake, to encrypt an HTTP response message 412 sent to client 402 in response to message 410.

One or more intermediary networking devices (e.g., a switch, router, firewall, etc.) located along the path between client 402 and service 404 may capture telemetry data from messages 406-412 of encrypted session 400. For example, ClientHello message 406 may include information such as the version of TLS that client 402 wishes to use, a sessionID, the ciphersuite(s) offered, the compression method, TLS extensions such as Server Name Indication (SNI), Heartbeat, and the like.

As noted above, identifying the underlying process that creates a TLS connection can be useful from both a security standpoint and a networking standpoint. Notably, identification of the underlying process can be used to detect malware, vulnerable executables, and the like. In addition, knowledge of the process can also be used to prioritize traffic, aid in diagnostics (e.g., analyzing how the process communicates across different network segments, etc.), etc.

One approach to identifying the process associated with encrypted traffic is to perform man-in-the-middle traffic inspection. Under this approach, an intermediate proxy between the communicating endpoints essentially acts as each endpoint from the standpoint of the other. More specifically, when one endpoint requests a secure connection with another endpoint, the proxy captures this request and sends its own request on to the other endpoint. In doing so, each endpoint essentially thinks that it is communicating securely with the other endpoint, but is actually communicating instead with the proxy. This allows the proxy to decrypt the traffic and perform packet inspection on the traffic.

A man-in-the-middle proxy approach is not viable in all circumstances. Indeed, there are privacy considerations that may prevent the decryption of all encrypted traffic. Moreover, man-in-the-middle inspection of encrypted traffic becomes very hard with TLS 1.3, as the use of Diffie-Hellmann authenticated handshakes prevents the inspection for legitimate and malicious content alike. While this provides end-to-end confidentiality and integrity protection, it also prevents any effective security inspection of the content.

TLS Fingerprinting for Process Identification

The techniques herein allow for the identification of an executable process associated with an encrypted traffic session without actually decrypting the traffic. In some aspects, the techniques herein can be used to construct an enhanced TLS fingerprint database with process and contextual data captured in any number of networks and updated with fused telemetry data from the network and/or the endpoint. In turn, a model may be constructed on top of the fingerprint database that identifies the most probable process given a new TLS session or set of TLS sessions. Doing so allows a network operator to solve security and networking issues by providing enhanced visibility into the endpoint processes that create the TLS sessions.

Specifically, according to one or more embodiments of the disclosure as described in detail below, a device obtains telemetry data regarding an encrypted traffic session in a network. The telemetry data includes Transport Layer Security (TLS) features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session. The device retrieves, using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes. The device uses a probabilistic model to assign probabilities to each of the plurality of candidate processes. The device identifies one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the process identifier 248, which may include computer executable instructions executed by the processor 220 (or independent processor of interfaces 210) to perform functions relating to the techniques described herein.

Operationally, the techniques herein leverage the concept of a TLS fingerprint, to identify the underlying process that initiates an encrypted TLS session in a network. In general, a ‘fingerprint’ is a sequence of bytes formed by parsing (some of) the fields of a network session, selecting some bytes from some of those fields, and then optionally normalizing them. Similarly, a ‘signature’ is a rule that is based on the fields of packet headers (including destination port) and patterns that can appear in the data stream of the session, or in a particular field. Likewise, a ‘watchlist’ refers to a set of IP addresses that identifies compromised or malicious Internet servers, or other important categories of devices.

FIG. 5 illustrates an example architecture 500 for identifying the process that initiated an encrypted traffic session in a network, according to various embodiments. At the core of architecture 500 is process identifier 248 which may comprise the following components: a TLS fingerprint database 502 and a process classifier 504. These components 502-504 may be implemented in a distributed manner or implemented on a single device. In addition, some or all of components 502-504 may be implemented as part of a monitored network (e.g., at the network edge, internal to the network, etc.) or part of a cloud-based device classification service. For example, in some implementations, a cloud-based device classification service may receive telemetry data captured from a network and return an indication of the identified process back to the network. The functionalities of the components 502-504 of architecture 500 may also be combined, omitted, or implemented as part of other processes, as desired.

As shown, process identifier 248 may receive telemetry data 506 a-506 b from any number of devices in the network regarding an encrypted traffic session. For example, process identifier 248 may receive telemetry data 506 a-506 b from any number of switches, routers, firewalls, or other intermediate networking devices located between endpoints of the traffic session. In further cases, process identifier 248 may receive at least a portion of telemetry data 506 a-506 b directly from an endpoint of the encrypted session.

According to various embodiments, process identifier 248 may construct and maintain a TLS fingerprint database 502 based on the received telemetry data 506 a-506 b. In some embodiments, a fingerprint in TLS fingerprint database 502 may take the form of a string, such as an octet string, derived from observations of a single network session by extracting carefully selected substrings from that data stream in such a way that the fingerprint is identical across each session initiated by a particular process.

While TLS fingerprinting alone can help to identify the process associated with an encrypted traffic session, one observation is that two or more processes may have the same TLS fingerprint. As used herein, a ‘process family’ refers to a set of related processes that all have the same fingerprint. More specifically, a process family includes distinct applications that share some software in common, or that are functionally equivalent. By way of example, Firefox version 68 for Linux, Mac, and Windows may all belong to the same process family. Additionally, there can also be more than one process family associated with a single fingerprint, leading to potential classification issues when relying on TLS telemetry data 506 a alone.

According to various embodiments, the techniques herein address the above challenges by augmenting the captured TLS fingerprint data in TLS telemetry data 506 a for an encrypted session with auxiliary/contextual information in auxiliary telemetry data 506 b regarding the destination and timing information for the session (e.g., time and/or date). For example, telemetry data 506 b may include any or all of the following information:

-   -   The destination IP address of the encrypted session     -   The destination port of the encrypted session     -   The destination host/server name (e.g., from the SNI extension)     -   Timing information for the session, such as the day and time of         the session

As shown, process identifier 248 may construct TLS fingerprint database 502 based on the obtained telemetry data 506 a-506 b. When implemented as part of a cloud service, TLS fingerprint database 502 may be constructed from potentially billions of observed encrypted sessions across any number of different networks. In addition to the name and hash (e.g., SHA-256) of each process, database 502 may also include a set of contextual data observed for a given process/TLS fingerprint pair:

-   -   A list of destination IP addresses (with their frequency count)     -   A list of destination ports (with their frequency count)     -   A list of server names taken from the server_name extension         (with their frequency count)     -   Overall counts, and counts limited to recent time windows (e.g.,         last 30 days)

In other words, process identifier 248 may construct database 502 by obtaining telemetry data 506 a-506 b regarding many network sessions. Then, for each session, process identifier 248 may extract the TLS fingerprint from TLS telemetry data 506 a and then incrementing the frequency count of each destination address, destination port, and/or server name indicated by auxiliary telemetry data 506 b regarding the encrypted session.

More specifically, in some embodiments, the schema for TLS fingerprint database 502 may include four main sections: 1.) a set of metadata fields that provide some general information about the fingerprint, 2.) TLS features, 3.) process information, and 4.) OS information. An overview of an example schema for database 502 is as follows:

{ “str_repr”: “. . .”, // Fingerprint metadata objects “md5_repr”: “. . .”, “source”: [“. . .”], “count”: z, “count_nvm”: y, “count_dmz”: x, “count_tg”: w, “max_implementation_date”: “. . .”, “min_implementation_date”: “. . .”, “tls_features”: {. . .}, // Human-readable description of TLS features “process_info”: [. . .], // List of associated process information objects “os_info”: [. . .], // List of associated OS information objects }

In some embodiments, the schema of TLS fingerprint database 502 may also include information captured from a malware and/or threat intelligence service, such as ThreatGrid by Cisco Systems, Inc. For example, such information may include the process objects or OS objects used by the service, allowing for greater integration between process identifier 248 and the service.

An example of the schema for the metadata fields is as follows:

{ “str_repr”: “(0301)(00390038003300320035002f00ff)((0000)(0023))”, “md5_repr”: “89f96e9f19fd8754d9965ede7937a7b3”, “source”: [“dmz”,“nvm”,“tg”], “count”: 691285, “count_nvm”: 124081, “count_dmz”: 566208, “count_tg”: 996, “max_implementation_date”: “2010-02”, “min_implementation_date”: “2002-06”, . . . }

Similarly, an example schema for the TLS features is as follows:

{ . . . “tls_features”: { “version”: “TLS 1.0”, “cipher_suites”: [ “TLS_DHE_RSA_WITH_AES_256_CBC_SHA”, “TLS_DHE_DSS_WITH_AES_256_CBC_SHA”, “TLS_DHE_RSA_WITH_AES_128_CBC_SHA”, “TLS_DHE_DSS_WITH_AES_128_CBC_SHA”, “TLS_RSA_WITH_AES_256_CBC_SHA”, “TLS_RSA_WITH_AES_128_CBC_SHA”, “TLS_EMPTY_RENEGOTIATION_INFO_SCSV” ], “extensions”: [ {“server_name”: “”}, {“session_ticket”: “”} ] }, . . . }

The process information, which may be included in telemetry data 502 b, may be captured and reported by an endpoint agent executed locally by the endpoint client. For example, Network Visibility Module (NVM) by Cisco Systems, Inc., or another suitable monitoring mechanism, may capture information about the process that initiates an encrypted traffic session in the network. An example of the schema for the process information in TLS fingerprint database 502 is as follows:

{ . . . “process_info”: [ { “process”: “Python”, “application_category”: “programming”, “count”: 82383, “prevalence”: 0.664, “first_seen”: “2018-05-07”, “last_seen”: “2018-05-18”, “count_last_7_days”: 0, “count_last_30_days”: 0, “sha256”: “A45244F6DCF841A6C165438BB16D074487A3BB1E83D705CBFB24690C3E09DCA E”, “sha_db”: { “filename”: “Python”, “file_version”: “2.7.10”, “product_name”: “Python”, “product_version”: “2.7.10”, “CVE_info”: [ “CVE-2017-1000158”, . . . ] }, “unique_hosts”: 1, “hostnames”: { “foo.test”: 49894, “coincheck.com”: 18016, . . . }, “dst_ips”: { “203.167.51.36”: 49894, “52.222.206.238”: 7638, . . . }, “dst_port”: { “443”: 82383, } }, . . . ], . . . }

In cases in which the endpoint client executes a monitoring agent, the process information can be further augmented with additional information. For example, below shows an example of the schema for such process information:

{ . . . “process_info”: [ { “filename”: “2361757781.exe”, “sha1”: “0ccaee90325287d4de4e3a73a2738bfadbe97692”, “count”: 4, “prevalence”: 0.0046, “av_sigs”: [ “Riskware.Win32.Lazagne.esgdvk”, . . . ], “hostnames”: { “www.python.org”: 2, . . . }, “dst_ips”: {“23.253.135.79”: 2, . . . }, “dst_ports”: { “443”: 4 } }, . . . ], . . . }

Finally, an example of the schema for the OS information from auxiliary telemetry data 506 b is as follows:

{ . . . “os_info”: [ { “os”: “Mac OS X”, “os_version”: “10.11.6”, “os_edition”: “El Capitan”, “count”: 25202, “prevalence”: 0.9889, }, . . . ] }

According to various embodiments, process identifier 248 may also include process classifier 504 configured output an indication of an identified process 508 for a particular encrypted traffic session. For example, for a given TLS ClientHello message observed in the network, the message may be transformed into a string representation that acts as a unique key that process classifier 504 can use to query TLS fingerprint database 502. This allows process classifier 504 to retrieve the list of probable process(es) associated with the key, along with their auxiliary/contextual information. For each of these processes, process classifier 504 may calculate a normalization factor that describes the prior probability of each process given the fingerprint, i.e., the probability of a process before the target session is considered. As a simple example, the normalization factor may be of the form:

-   -   process [“count_last_30_days”]/fingerprint[“count_last_30_days”]

The above gives more weight to processes that were observed using a specific fingerprint over the last 30 days. In this context, “last 30 days” indicates the 30-day period prior to when the session was observed. In further embodiments, process classifier 504 may use other models, to predict the probability of a process. For instance, instead of tracking the count of each process for each day, that distribution could be approximated as a Gaussian, a one-sided Gaussian, or another probability distribution.

Next, process classifier 504 may then extract session specific data, to increase the fidelity of the initial estimate. More specifically, process classifier 504 may extract three fields: destination IP address, destination port, and the server_name extension data. For each field, process classifier 504 may assign a probability to each process based on the observed session's data and the contextual data contained within the process's fingerprint entry in database 502 indicating how probable a process is given the observed data. From this, process classifier 504 may compute these values by dividing the occurrences of the process/field pair by the total count of the process.

By way of example, assume that a TLS fingerprint has been associated with both Microsoft Outlook and Firefox. Additionally, for this specific fingerprint, the contextual data indicates that Microsoft Outlook goes to “nexusrules.officeapps.live.com” in 30% of its sessions and Firefox goes to “nexusrules.officeapps.live.com” in less than 1% of its session. The per-process server_name probabilities will thus lead to process classifier 504 preferring Microsoft Outlook, in this case.

Process classifier 504 can then combine the above information, to assign a single value to each process. In turn, process classifier 504 may use these values to select the most likely process from the list of processes given by the fingerprint and output the result as identified process 508. To do so, process classifier 504 may, for each session-specific field, multiply the field by a field-specific weight, and then create a linear combination of the different fields for each process. A domain expert can assign the field-specific weights, or an optimization procedure can assign the weights based on the available data, in various embodiments. Finally, process classifier 504 may multiply the linear combination by the normalization factor and report the process with the maximum value as identified process 508. Additional data features that can also be used by process classifier 504 for this such as, for example, a Boolean value that indicates whether the destination port is different than 443 (the standard TLS port). The computation by process classifier 504 to identify the most probable process can either be done online or can be pre-computed given the input data features.

In further embodiments, rather than simply assess encrypted flows as they occur (e.g., based on their observed ClientHello messages), process classifier 504 may also take into account state information about previously seen connections, as well. Note that the contextual/auxiliary data for a process/fingerprint pair, such as destination IP address, port, and server name, is often very informative. However, this is not always the case. For example, Chrome and Firefox will visit similar sites because a user is driving the majority of the generated traffic. But, both of these processes have a non-negligible set of control traffic, or indicators, that visits servers that are very informative for process identification (e.g., *.mozilla.org).

Accordingly, process identifier 248 may also incorporate multiple sessions into its analysis, to build up an inventory of applications for a specific endpoint, and let that inventory inform future process identifications. For example, if process identifier 248 has seen many browser-related sessions from an endpoint, the smaller set of indicator flows can help to adjust the process-specific scores, so that they are more aligned with past observations. In one embodiment, process classifier 504 can also incorporate the sequential nature of network traffic by modeling the TLS traffic from an endpoint as a Markov process. If, for example, process identifier 248 observes an initial connection from Firefox to *.mozilla.org, and then observes a connection to *.google.com from the same endpoint, process classifier 504 can incorporate the probability of seeing two consecutive Firefox flows into is process identification decision, as opposed to a Firefox flow and then a googleupdate.exe flow.

More formally, process classifier 504 may take the form of a Bayesian classifier, in some embodiments. To do so, each session may be associated with a process z∈Z, which is not directly observed, as well as the observed data features f₁, f₂, . . . , f_(n). To provide a concrete example with n=4 features, let f₁ denote the TLS fingerprint, f₂ the destination address, f₃ the destination port, and f₄ the server name. The goal, then, is to make a classifier c that returns the most probable process for the feature values (f₁, . . . , f_(n)). To ensure that the output of the classifier is interpretable, more complex machine learning approaches may be eschewed in favor of computing the Bayesian probability estimates for each process and return that with the highest probability as follows:

$\begin{matrix} {{e\left( {f_{1},\ldots\mspace{14mu},f_{n}} \right)} = {\underset{z \in Z}{argmax}\mspace{11mu}{P\left( {{z❘f_{1}},\ldots\mspace{14mu},f_{n}} \right)}}} & {(1)} \\ {= {\underset{z \in Z}{argmax}\mspace{11mu}{P\left( {f_{1},\ldots\mspace{14mu},{f_{n}❘z}} \right)}\frac{P(z)}{P\left( {f_{1},\ldots\mspace{14mu},f_{n}} \right)}}} & {(2)} \\ {= {\underset{z \in Z}{{argmax}\;}\;{P\left( {f_{1},\ldots\mspace{14mu},{f_{n}❘z}} \right)}{P(z)}}} & {(3)} \\ {= {\underset{z \in Z}{argmax}\mspace{11mu}{P(z)}{\prod\limits_{{i = 1},n}\;{P\left( {f_{i}❘z} \right)}}}} & {(4)} \\ {= {\underset{z \in Z}{{argmax}\mspace{11mu}\log}{P(z)}{\sum\limits_{{i = 1},n}{\log\;{{P\left( {f_{i}❘z} \right)}.}}}}} & {{~~~~~~~~~~~~~}(5)} \end{matrix}$

Equation (1) above simply defines the classifier c as the function that returns the most probable process for the feature list. Equation (2) applies Bayes' theorem and equation (3) removes the irrelevant denominator. Equation (4) then applies the ‘naive Bayes’ assumption that each observed feature is conditionally independent from all other observed features. Finally, equation (5) simplifies the computation by moving to sums of logarithms.

The naive Bayes assumption is useful because it is far easier to accurately estimate the probability P(f_(i)|z) from a set of observations, as opposed to estimating P(f1, . . . , fn|z). There are several extensions to the naive Bayes approach that can improve its performance while retaining its computational simplicity, such as tree-augmented and hidden naïve Bayes that process classifier 504 can also use, in further embodiments.

To compute equation (5) above, process classifier 504 must first estimate P(f_(i)|z) from the previously observed telemetry data 506. For example, suppose that the destination IP address 17.178.96.59 is observed in an encrypted session. If, in the prior observations, that address had been seen N_(a) times for process a and N_(b) times for process b, the estimation of P(17.178.96.59|z=a) is straight-forward. However, there is no direct way for process classifier 504 to estimate P(f_(i)|z) for any value of f_(i) that is not in the set of prior observations. This is problematic because network data features are quite sparse. Indeed, the vast majority of Internet traffic visits only a small fraction of the addresses, server names, and ports on the Internet.

As would be appreciated, the fundamental goal of machine learning is to generalize beyond the examples in the training set and make accurate predictions about instances that are not in the set. However, it is impossible to apply interpolation to categorical variables such as addresses, ports, and names. A pair of distinct port values that are numerically close may have semantic meanings that are entirely different, for instance.

The techniques herein address the issue of data sparseness by introducing a way for process classifier 504 to determine the equivalence classes of those features using auxiliary telemetry data 506 b and a way to use those equivalence classes for purposes of identifying the process associated with an encrypted session. Continuing the example above, assume that destination IP address 17.178.96.59 was observed in a session but does not appear in the prior observation set. In one embodiment, auxiliary telemetry data 506 b may further include data obtained from the Domain Name System (DNS). For example, based on this, the server name apple.com may be associated with the three addresses: 17.178.96.59, 17.142.160.59, and 17.172.224.47, as well as the server names: apple.by, appleid.org, carbonapi.com, carbontest.com, dar-wincode.com, appleiphone.com, and expertapple.com.

More formally, process identifier 248 may treat the addresses 17.178.96.59, 17.142.160.59, and 17.172.224.47 are equivalent based on their DNS relationship, which can be denoted as follows:

-   -   γDNS(17.178.96.59)=γDNS(17.142.160.59)=γDNS(17.172.224.47)         where the function γDNS maps an address to its equivalence         class. Similarly, process identifier 248 may use a function,         γRDNS, that maps a server name to its Reverse DNS equivalence         class. By way of example, all of the DNS names listed above         belong to the same class.

Each equivalence relation partitions the set of features into distinct subsets. There may also be more than one useful equivalence relation for a feature, since there are multiple ways that addresses, ports, and server names can be related. All of the addresses within a BGP Autonomous System (AS) are equivalent in some way, as are the addresses in a corporate offering such as Microsoft Office365, Azure, Amazon Web Services (AWS), or Cloudflare. The companies in these cases publish the address ranges of interest. Accordingly, process identifier 248 may also leverage equivalence functions γAS( ) and γowner( ) for these relations, in some embodiments. In a further embodiment, another useful equivalence relation is the prevalence with which a data feature, such as the rank of webservers.

In various embodiments, the classifier of equations (1)-(5) above can also be generalized beyond prior observations by using the assumption that the probability that an observable feature has a particular value depends only on its equivalence classes. Letting γ_(i) ^(J) denote the j^(th) equivalence function for the i^(th) feature, the assumption is that:

$\begin{matrix} {{P\left( {f_{i}❘z} \right)} = {\prod\limits_{{j = 1},p}{{P\left( {{\gamma_{i}^{j}\left( f_{i} \right)}❘z} \right)}.}}} & (6) \end{matrix}$

Using this assumption in equation (4) and converting to logarithms gives the generalized classifier g as follows:

$\begin{matrix} {{g\left( {f_{1},\ldots\mspace{14mu},f_{n}} \right)} = {\underset{z \in Z}{argmax}{P(z)}{\prod\limits_{{i = 1},n}{\prod\limits_{{j = 1},p_{i}}{P\left( {{\gamma_{i}^{j}\left( f_{i} \right)}❘z} \right)}}}}} & {(7)} \\ {{\underset{z \in Z}{= {argmax}}\mspace{11mu}\log\mspace{11mu}{P(z)}{\sum\limits_{{i = 1},n}{\sum\limits_{{j = 1},p_{i}}{\log\;{P\left( {{\gamma_{i}^{j}\left( f_{i} \right)}❘z} \right)}}}}},} & {{~~~~~}(8)} \end{matrix}$ where p_(i) denotes the number of equivalence functions used for the i^(th) feature.

Returning again to the above example, if 17.178.96.59 had not been observed in the training set for process classifier 504, but other addresses in its equivalence class had been, then the classifier c would not know how to estimate the probabilities associated with that destination, whereas g would assign probabilities based on the observations of equivalent addresses. As long as some of the addresses in each equivalence class appear in the prior data, all of the equivalence classes will be represented, and the process identifier 248 can handle sessions bound to any destination on the Internet. While there is no guarantee that all classes will be represented, the DNS, RDNS, AS, and/or owner equivalence classes all greatly expand the coverage of the empirical estimates. Note that, if needed, process identifier 248 can also apply smoothing to empirical estimates to compensate for unobserved classes.

This approach relies on the auxiliary telemetry data 506 b used to compute DNS, RDNS, AS, and/or owner equivalence classes being accurate and timely. In practice, this data 506 b may need to be proactively obtained, in some cases, relying either on a mechanism that pushes data 506 b to process identifier 248 or a pull mechanism whereby process identifier 248 proactively requests this information.

Pseudocode for the computation of a generalized classifier g using equivalence classes is as follows, in one embodiment:

proc_list.initialize_to_empty_list( ) for z in processes do q ← log P(z) for i from 1 to n do for j from 1 to p_(i) do c ← γ_(i) ^(J)(f_(i)) q ← q + log P(c | z) end for end for proc_list.append(q, z) end for return proc_list.get_maximum( )

Several optimizations on the above approach are also possible, in further embodiments. For example, the outer loop over z can be vectorized, since each operation inside that loop applies the same operations to the distinct z values. Additionally, there can be a single function for each data feature that takes that feature as input and returns all of the equivalence class values associated with that feature. Of course, the inner loops can be unrolled, too.

Computing the equivalence classes for a destination IP address involves finding all of the subnets associated with an equivalence class that match that address. Indeed, as subnets can be nested, an address can be associated with multiple subnets. The problem of finding all matching subnets is similar to the longest subnet prefix match problem. Accordingly, a natural approach would be to use a PATRICIA trie or radix trie, which can be extended to find all matching prefixes, as is needed here.

Another important optimization would be to condition the probabilities on the TLS fingerprint, and organize the computation so that it performs computation only for the processes associated with a given fingerprint, in a further embodiment. Since the number of process associated with a typical fingerprint is orders of magnitude smaller than the overall number of processes, this optimization significantly reduces computational cost. The below pseudocode illustrates this approach and also unrolls the inner loops of the above (but does not vectorize the outer loop). It makes use of the function get_process_list( ) that takes a fingerprint as input and returns the list of processes associated with it. Each of the functions get_alexa_rank( ) get cloud_provider( ), get_asn_info( ), and get_port_category( ) takes as input a feature and returns the equivalence class for that feature. The function log_prob_fingerprint( ) returns the log of the probability of a process for the fingerprint provided as its input, and each of the functions log_prob_alexa( ) log_prob_cloud( ), log_prob_asn( ), log_prob_port_cat( ) take as input a process and an equivalence class and return the log of the probability for that pair of events.

proc_list.initialize_to_empty_list( ) alexa_class ← get_alexa_rank(server_name) cloud_class ← get_cloud_provider(dest_addr) asn_class ← get_asn_info(dest_addr) port_class ← get_port_class(dest_port) for z in get_process_list(ÿngerprint) do lp ← log_prob_ÿngerprint(z, ÿngerprint) lp ← lp + log_prob_alexa(z, alexa_class) lp ← lp + log_prob_cloud(z, cloud_class) lp ← lp + log_prob_asn(z, asn_class) lp ← lp + log_prob_port_class(z, port_class) proc_list.append(lp, z) end for return proc_list.get_maximum( ))

FIG. 5 illustrates an example simplified procedure for identifying a process that initiated an encrypted traffic session, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device may perform procedure 500 by executing stored instructions, such as process identifier 248. The procedure 500 may start at step 505, and continues to step 510, where, as described in greater detail above, the device may obtain telemetry data regarding an encrypted traffic session in a network. In various embodiments, the telemetry data comprises TLS features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session. For example, the device may receive the telemetry data from any number of intermediary networking devices between the endpoints of the encrypted traffic session. In a further case, the device may itself be an intermediary network device through which the session flows and capture the telemetry data. In various embodiments, the telemetry data may be captured without actually decrypting the encrypted traffic.

At step 515, as detailed above, the device may retrieve, using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes. For example, the device may transform the telemetry data into a string representation and use this representation as a lookup key for the TLS fingerprint database. In general, the fingerprint database may include entries that relate processes with the TLS and auxiliary telemetry data from their encrypted traffic sessions.

At step 520, the device may use a probabilistic model to assign probabilities to each of the plurality of candidate processes, as described in greater detail above. In various embodiments, the model make take the form of a Bayesian classifier, such as a naïve Bayesian classifier, a tree-augmented classifier, or a hidden naïve Bayesian classifier. According to further embodiments, this may also entail the device assigning at least one of the destination address, port, or server name from the auxiliary information in the obtained telemetry data to an equivalence class. For example, the equivalence class may associate addresses, ports, and/or server names that are related by belonging to the same owner (e.g., a business or other entity) or autonomous system.

At step 525, as detailed above, the device may identify one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability. Notably, the device may determine that the process with the highest assigned probability is the process that initiated the encrypted traffic session. This information is quite useful and can be used, for example, to determine that the identified process comprises malware or represents a security threat, such as a version of software with a known vulnerability. Procedure 500 then ends at step 530.

It should be noted that while certain steps within procedure 500 may be optional as described above, the steps shown in FIG. 5 are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein.

The techniques described herein, therefore, allow for the identification of a process that initiated an encrypted traffic session in a network. In some aspects, leveraging auxiliary information, such as the destination address, port, or server name associated with the session can aid in better matching the session to a process (e.g., an application or other software component).

While there have been shown and described illustrative embodiments that provide for the identification of a process that initiated an encrypted traffic session, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to using certain models for purposes of this identification, the models are not limited as such and may be used for other functions, in other embodiments. In addition, while certain protocols are shown, such as TLS, other suitable protocols may be used, accordingly.

The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein. 

What is claimed is:
 1. A method comprising: obtaining, by a device, telemetry data regarding an encrypted traffic session in a network, wherein the telemetry data comprises Transport Layer Security (TLS) features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session; retrieving, by the device and using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes; using, by the device, a probabilistic model to assign probabilities to each of the plurality of candidate processes; and identifying, by the device, one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability.
 2. The method as in claim 1, wherein the TLS features of the traffic session comprise at least one of: a ciphersuite or TLS version.
 3. The method as in claim 1, wherein the probabilistic model comprises a Bayesian classifier.
 4. The method as in claim 1, further comprising: assigning the destination address of the traffic session, the destination port of the traffic session, or the server name associated with the traffic session with an equivalence class, based on its relationship to other addresses, ports, or server names in the TLS fingerprint database.
 5. The method as in claim 4, wherein the equivalence class comprises addresses, ports, or server names associated with the same owner or autonomous system.
 6. The method as in claim 4, wherein the probabilistic model assigns probabilities to each of the plurality of processes based in part on the equivalence class.
 7. The method as in claim 1, further comprising: determining that the identified process comprises malware or represents a security threat.
 8. An apparatus, comprising: one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed configured to: obtain telemetry data regarding an encrypted traffic session in the network, wherein the telemetry data comprises Transport Layer Security (TLS) features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session; retrieve, using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes; use a probabilistic model to assign probabilities to each of the plurality of candidate processes; and identify one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability.
 9. The apparatus as in claim 8, wherein the TLS features of the traffic session comprise at least one of: a ciphersuite or TLS version.
 10. The apparatus as in claim 8, wherein the probabilistic model comprises a Bayesian classifier.
 11. The apparatus as in claim 8, wherein the process when executed is further configured to: assign the destination address of the traffic session, the destination port of the traffic session, or the server name associated with the traffic session with an equivalence class, based on its relationship to other addresses, ports, or server names in the TLS fingerprint database.
 12. The apparatus as in claim 11, wherein the equivalence class comprises addresses, ports, or server names associated with the same owner or autonomous system.
 13. The apparatus as in claim 11, wherein the probabilistic model assigns probabilities to each of the plurality of processes based in part on the equivalence class.
 14. The apparatus as in claim 8, wherein the process when executed is further configured to: determine that the identified process comprises malware or represents a security threat.
 15. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device in a network to execute a procedure comprising: obtaining, by the device, telemetry data regarding an encrypted traffic session in a network, wherein the telemetry data comprises Transport Layer Security (TLS) features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session; retrieving, by the device and using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes; using, by the device, a probabilistic model to assign probabilities to each of the plurality of candidate processes; and identifying, by the device, one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability.
 16. The computer-readable medium as in claim 15, wherein the TLS features of the traffic session comprise at least one of: a ciphersuite or TLS version.
 17. The computer-readable medium as in claim 15, wherein the probabilistic model comprises a Bayesian classifier.
 18. The computer-readable medium as in claim 15, wherein the procedure further comprises: assigning the destination address of the traffic session, the destination port of the traffic session, or the server name associated with the traffic session with an equivalence class, based on its relationship to other addresses, ports, or server names in the TLS fingerprint database.
 19. The computer-readable medium as in claim 18, wherein the equivalence class comprises addresses, ports, or server names associated with the same owner or autonomous system.
 20. The computer-readable medium as in claim 18, wherein the probabilistic model assigns probabilities to each of the plurality of processes based in part on the equivalence class. 